Higher-Order Peridynamic Material Correspondence Models for Elasticity

被引:9
|
作者
Chen, Hailong [1 ]
Chan, WaiLam [1 ]
机构
[1] Univ Kentucky, Dept Mech Engn, Lexington, KY 40506 USA
关键词
Peridynamics; Material correspondence model; Higher-order deformation gradient; Length-scale effect; Size dependence; Strain gradient theory; STRAIN GRADIENT THEORY; CRACK-GROWTH; DEFORMATION GRADIENTS; STRESS; SIZE; DISLOCATIONS; LOCALIZATION; SIMULATIONS; FORMULATION; STRENGTH;
D O I
10.1007/s10659-020-09793-6
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Higher-order peridynamic material correspondence model can be developed based on the formulation of higher-order deformation gradient and constitutive correspondence with generalized continuum theories. In this paper, we present formulations of higher-order peridynamic material correspondence models adopting the material constitutive relations from the strain gradient theories. Similar to the formulation of the first-order deformation gradient, the weighted least squares technique is employed to construct the second-order and the third-order deformation gradients. Force density states are then derived as the Frechet derivatives of the free energy density with respect to the deformation states. Connections to the second-order and the third-order strain gradient elasticity theories are established by realizing the relationships between the energy conjugate stresses of the higher-order deformation gradients in peridynamics and the stress measures in strain gradient theories. In addition to the horizon, length-scale parameters from strain gradient theories are explicitly incorporated into the higher-order peridynamic material correspondence models, which enables application of peridynamics theory to materials at micron and sub-micron scales where length-scale effects are significant.
引用
收藏
页码:135 / 161
页数:27
相关论文
共 50 条
  • [31] On learning higher-order cumulants in diffusion models
    Aarts, Gert
    Habibi, Diaa E.
    Wang, Lingxiao
    Zhou, Kai
    MACHINE LEARNING-SCIENCE AND TECHNOLOGY, 2025, 6 (02):
  • [32] Higher-order statistical models of visual images
    Simoncelli, EP
    PROCEEDINGS OF THE IEEE SIGNAL PROCESSING WORKSHOP ON HIGHER-ORDER STATISTICS, 1999, : 54 - 57
  • [33] Higher-order anisotropic models in phase separation
    Cherfils, Laurence
    Miranville, Alain
    Peng, Shuiran
    ADVANCES IN NONLINEAR ANALYSIS, 2019, 8 (01) : 278 - 302
  • [34] Rational Expectations Models with Higher-Order Beliefs
    Huo, Zhen
    Takayama, Naoki
    REVIEW OF ECONOMIC STUDIES, 2024,
  • [35] The Higher-Order Sobolev-Type Models
    Zamyshlyaeva, A. A.
    BULLETIN OF THE SOUTH URAL STATE UNIVERSITY SERIES-MATHEMATICAL MODELLING PROGRAMMING & COMPUTER SOFTWARE, 2014, 7 (02): : 5 - 28
  • [36] Dual equivalence in models with higher-order derivatives
    Bazeia, D
    Menezes, R
    Nascimento, JR
    Ribeiro, RF
    Wotzasek, C
    JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL, 2003, 36 (38): : 9943 - 9959
  • [37] PERTURBED HIGHER-ORDER POPULATION-MODELS
    BOJADZIEV, G
    MATHEMATICAL MODELLING, 1987, 8 : 772 - 777
  • [38] Models of higher-order structure: foldamers and beyond
    Cubberley, MS
    Iverson, BL
    CURRENT OPINION IN CHEMICAL BIOLOGY, 2001, 5 (06) : 650 - 653
  • [39] Uprooting and Rerooting Higher-Order Graphical Models
    Rowland, Mark
    Weller, Adrian
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017), 2017, 30
  • [40] LOCALIZED MAGNETIC MODELS WITH HIGHER-ORDER INTERACTIONS
    Stubna, Viliam
    Jascur, Michal
    ACTA PHYSICA SLOVACA, 2019, 69 (02) : 75 - 147